Abstract
To confront the Resource Constrained Project Scheduling Problem (RCPSP), metaheuristics have been proved very good alternatives, especially for large complicated projects. In this class of algorithms, Evolutionary Computation has recently gained much attention, with most important representative the Genetic Algorithms. Following the mainstream, we stress our efforts on another evolutionary algorithm, the Evolution Strategies (ES). The application of ES takes place under two discrete solution encodings; one works on vectors of priority values and the other is based on convex combinations of priority rules. The analysis of the results, produced from tests on the PSPLIB, inspired the development of two extended algorithms. The first extension assumes that ES work on vectors of priority values but the underlying evolutionary operators are modified so as to allow a fast reordering of activities. The second extension concerns the construction of a novel solution encoding which combines the priority values and the convex combination of priority rules. Both proposals indicate a far better performance when compared with genetic algorithms, hence, open a new research direction in the domain of project scheduling with evolutionary algorithms.
Similar content being viewed by others
References
Alcaraz, Z. and Maroto, C. (2001). A Robust Genetic Algorithm for Resource Allocation in Project Scheduling. Annals of Operations Research vol. 102, 83–109.
Alvarez-Valdes R. and Tamarit J.M. (1989). Heuristic Algorithms for Resource Constrained Project Scheduling: A Review and an Empirical Analysis. in Advances in Project Scheduling, (R. Slowinski and J. Weglarz ed.) Elsevier, Amsterdam, 113–134.
Back, T. (1996). Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York.
Blickle, T. (2000). Tournament Selection, in Evolutionary Computation: Basic Algorithms and Operations vol. 1, (Back, T., Fogel, D.B. and Michalewicz, T., ed.) Institute of Physics Publishing, Bristol, 181–186.
Brucker, P., Drexl, A., Mohring, R., Neumann, K. and Pesch, E. (1999). Resource Constrained Project Scheduling: Notation, Classification, Models and Methods. Europ. Journ. of Oper. Res. vol. 112, 3–41.
Eshelman, L.J. (2000). Genetic Algorithms, in Evolutionary Computation: Basic Algorithms and Operations vol. 1, (Back, T., Fogel, D.B. and Michalewicz, T., ed.) Institute of Physics Publishing, Bristol, 64–80.
Grefenstatte, J. (2000). Proportional Selection and Sampling Algorithms, in Evolutionary Computation: Basic Algorithms and Operations vol. 1, (Back, T., Fogel, D.B. and Michalewicz, T., ed.) Inst of Physics Publishing, Bristol, 172–180.
Hartmann, S. (1998). A Competitive Genetic Algorithm for Resource Constrained Project Scheduling. Naval Research Logistics vol. 45, 733–750.
Hartmann, S. and Kolisch, R. (2000). Experimental Evaluation of State of the Art Heuristics for the Resource Constrained Project Scheduling Problem. European Journal of Operational Research vol. 127, 394–407.
Hindi, K.S. and Yang, H. and Flezar, K. (2002). An Evolutionary Algorithm for Resource Constrained Project Scheduling. IEEE Transactions on Evolutionary Computation vol. 6(5), 512–518.
Kolisch, R. (1996). Serial and Parallel Resource Constrained Project Scheduling Methods Revisited: Theory and Computation. European Journal of Operational Research vol. 90, 320–333.
Kolisch, R. and Hartmann, S. (1999). Heuristics Algorithms for Solving the Resource Constrained Project Scheduling Problem: Classification and Computational Analysis, in Project Scheduling: Recent Models, Algorithms and Applications, (J. Weglarz, ed.) Kluwer, Amsterdam, 147–178.
Kolisch, R. and Padman, R. (2001). An Integrated Survey of Deterministic Project Scheduling. Omega vol. 29, 249–272.
Kolisch, R. and Sprecher, A. (1996). PSPLIB: A Project Scheduling Problem Library. European Journal of Operational Research vol. 96, 205–216.
Lee, J.K. and Kim, Y.D. (1996). Search Heuristics for Resource Constrained Project Scheduling. Journal of Operational Research Society vol. 47, 678–689.
Merkle, D., Middendorf, M. and Schmeck, H. (2002). Ant Colony Optimization for Resource Constrained Project Scheduling. IEEE Transactions on Evolutionary Computation vol. 6(4), 333–346.
Rocha, M., Vileda, C., Cortez, P. and Neves, J. (2000). Viewing Scheduling Problems through Genetic and Evolutionary Algorithms. in Lecture Notes in Computer Science vol. 1800, (Rolim, J. et al., ed.) Springer Verlag, Berlin, 612–619.
Schwefel, H.P. and Rudolph, G. (1995). Contemporary Evolution Strategies. 3rd International Conference on Artificial Life, 893–907.
Thomas, P.R. and Salhi, S. (1997). An Investigation into the Relationship of Heuristic Performance with Network-Resource Characteristics. Journal of Operational Research Society vol. 48, 34–43.
Ulusoy, G. and Ozdamar, L. (1989). Heuristic Performance and Network/Resource Characteristics in Resource Constrained Project Scheduling. Journal of Operational Research Society. vol. 40(12), 1145–1152.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Diakoulakis, I.E., Koulouriotis, D.E. & Emiris, D.M. Resource Constrained Project Scheduling using evolution strategies. Oper Res Int J 4, 261–275 (2004). https://doi.org/10.1007/BF02944145
Issue Date:
DOI: https://doi.org/10.1007/BF02944145